new optimizer key

Author: sarthakpati

samples/config_all_options.yaml

@@ -83,7 +83,7 @@ weighted_loss: True
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

samples/config_classification.yaml

@@ -53,7 +53,7 @@ weighted_loss: True
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

samples/config_dfu2021_vgg11_without_preprocess_classification.yaml

@@ -52,7 +52,7 @@ weighted_loss: True
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

samples/config_regression.yaml

@@ -53,7 +53,7 @@ weighted_loss: True
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

samples/config_segmentation_brats.yaml

@@ -47,7 +47,7 @@ loss_function: dcce
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

samples/config_segmentation_histology.yaml

@@ -51,7 +51,7 @@ loss_function: dc
 #    }
 #  }
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # this parameter controls the nested training process
 # performs randomized k-fold cross-validation
 # split is performed using sklearn's KFold method

testing/config_classification.yaml

@@ -25,7 +25,7 @@ nested_training:
   testing: -2
   validation: -2
 num_epochs: 1
-opt: adam
+optimizer: adam
 parallel_compute_command: ''
 patch_sampler: uniform
 patch_size: 

testing/config_regression.yaml

@@ -23,7 +23,7 @@ nested_training:
   testing: -2
   validation: -2
 num_epochs: 1
-opt: adam
+optimizer: adam
 parallel_compute_command: ''
 patch_sampler: uniform
 patch_size: 

testing/config_segmentation.yaml

@@ -35,7 +35,7 @@ scheduler: triangle
 loss_function: dc
 weighted_loss: True
 # Which optimizer do you want to use - adam/sgd
-opt: adam
+optimizer: adam
 # the value of 'k' for cross-validation, this is the percentage of total training data to use as validation;
 # randomized split is performed using sklearn's KFold method
 # for single fold run, use '-' before the fold number